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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JIKO (Jurnal Informatika dan Komputer) ILKOM Jurnal Ilmiah Journal of Information Technology and Computer Engineering Patria Artha Technological Journal Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali JURNAL IT: Media Infromasi STMIK Handayani Makassar Jambura Journal of Electrical and Electronics Engineering Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Jurnal JEETech Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Information System Journal (INFOS) Bulletin of Information Technology (BIT) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Penelitian Ilmu Pendidikan Indonesia Nobel Community Services Journal Edumaspul: Jurnal Pendidikan
Hazriani, Hazriani
Universitas Handayani Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Klasifikasi Sapi Perah dan Non-Perah Menggunakan Algoritma Convolutional Neural Network: Classification of Dairy and Non-Dairy Cattle Using the Convolutional Neural Network Algorithm Maramis, Leonard; Nurtanio, Ingrid; Zainuddin, Hazriani
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1824

Abstract

Sapi merupakan salah satu hewan ternak utama di Indonesia yang terdiri dari sapi perah dan sapi potong. Di Kotamobagu dan Bolaang Mongondow Raya (BMR), peternakan sapi berkembang pesat seiring dengan meningkatnya daya beli masyarakat dan nilai jual sapi yang tinggi. Namun, transaksi jual-beli sapi masih menghadapi kendala, terutama dalam membedakan jenis sapi yang dapat menyebabkan kesalahan dan potensi penipuan. Penelitian ini bertujuan untuk mengimplementasikan algoritma Convolutional Neural Network (CNN) dengan arsitektur Xception dalam klasifikasi sapi perah dan non-perah. Proses penelitian mencakup pengumpulan data citra sapi, pelabelan, serta pelatihan model CNN untuk mengenali karakteristik fisik masing-masing jenis sapi. Hasil pengujian menunjukkan bahwa model Xception mencapai akurasi 96% dengan pembagian dataset 80:20, membuktikan kemampuannya dalam mengenali pola visual dengan baik. Temuan ini menunjukkan bahwa CNN, khususnya dengan arsitektur Xception, dapat menjadi alat yang efektif dalam identifikasi jenis sapi, sehingga berpotensi meningkatkan keamanan dan keakuratan dalam transaksi ternak. Dengan pengembangan lebih lanjut, sistem ini dapat diintegrasikan dengan teknologi kamera untuk pemantauan otomatis guna mendukung industri peternakan yang lebih modern dan efisien.
Peramalan Tren Penjualan Menu Restoran Menggunakan Metode Single Moving Average Apriliani, Aulia; Zainuddin, Hazriani; Agussalim, Agussalim; Hasanuddin, Zulfajri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722732

Abstract

Penelitian ini bertujuan untuk meramalkan tren penjualan menu pada restoran guna membantu pihak pengelola restoran dalam menentukan dan memberikan rekomendasi pengelolaan stok menu. Peramalan dilakukan dengan mengimplementasikan metode single moving average pada data transaksi penjualan selama periode 15 bulan, yakni bulan Januari-Desember 2018 dan Januari-Maret 2019 untuk menghasilkan ramalan bulanan dan harian. Total sampel data latih yang diolah sebanyak 10.515 record yang merupakan data transaksi penjualan pada bulan Januari-Desember tahun 2018, serta 2.246 record data bulan Januari-Maret 2019 sebagai data uji (untuk menguji akurasi ramalan). Hasil pengujian hasil ramalan bulanan untuk Top-10 menu menghasilkan perhitungan MAPE (Mean Absolut Percentage Error) sebesar 4% yang berarti tingkat akurasi sangat baik, yakni sebesar 96%. Sedangkan pengujian hasil ramalan harian menghasilkan MAPE yang cukup tinggi yaitu sebesar 39.2%, mengindikasikan nilai akurasi yang cukup rendah, yakni 60.8%. Meskipun akurasi untuk ramalan harian, masih rendah namun hasil penelitian ini dapat memberikan gambaran kepada pengelola hotel tentang rentang minimum-maksimal stok yang perlu disiapkan untuk menu tertentu pada hari-hari tertentu. Untuk memperoleh akurasi prediksi harian yang lebih akurat, penelitian ini akan dilanjutkan dengan mencoba metode lain serta menambah jumlah data latih. AbstractThis research aims to forecast sales trend of a restaurant menus to help the restaurant management in determaining and providing recommendations for managing stocks. Forecasting was performed by applying the single moving average towards fifteen months recorded data transaction, namely January to December 2018, and Januari to March 2019 to establish monthly and daily forecast. Total data training was 10.515 recods data transaction obtained from Januari to December 2018, while data testing was 2.246 record data transaction within Januari to March 2019. Result for montly forecast shows, that the average accuracy reached 96% (MAPE 4%) indicating the forecast is almost perfect. While, for daily forecast the average accuracy is only 60.8% (MAPE 39,2%) indicating that the forecast is less accurate. Although, accuracy of the daily forecast is considered less accurate, the result still can be used by the restaurant management to figure-out minimum and maximum amount of stock to be prepared for certain menus in certain days. 
Pengaruh Kecerdasan Intra Personal dan Partisipasi Aktif Siswa SMP N 1 Rantau Selatan Terhadap Prestasi Belajar Matematika Hazriani, Hazriani; harahap, Amin; harahap , Nurlina Ariani
Jurnal Penelitian Ilmu Pendidikan Indonesia Vol. 3 No. 2 (2024): Volume 3 No 2
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpion.v3i2.294

Abstract

Penelitian ini bertujuan untuk menyelidiki pengaruh kecerdasan intra personal dan keterlibatan aktif siswa dalam mencapai prestasi belajar matematika di SMP N 1 Rantau Selatan. Metode kuantitatif dengan pendekatan korelasional digunakan, dan informasi diperoleh melalui penggunaan kuesioner dan melalui pengamatan langsung terhadap siswa. Hasil analisis menunjukkan adanya korelasi positif antara kecerdasan intra personal siswa, partisipasi aktif, dan prestasi belajar matematika. Regresi linier berganda mengungkapkan bahwa kecerdasan intra personal dan partisipasi aktif siswa bersama-sama dapat memprediksi prestasi belajar matematika. Temuan ini memiliki implikasi praktis untuk pengembangan strategi pembelajaran yang mempertimbangkan aspek kecerdasan intra personal dan meningkatkan partisipasi aktif siswa. Oleh karena itu, disarankan untuk mengintegrasikan program pembinaan kecerdasan intra personal dan merancang metode pembelajaran yang mendorong partisipasi siswa secara aktif. Langkah-langkah ini diharapkan dapat meningkatkan kualitas pembelajaran matematika di SMP N 1 Rantau Selatan.
PERSONAL MOBILITY ASSISTANT UNTUK MENUNJANG MOBILITAS AMAN BAGI ANAK DI KOTA KENDARI MENGGUNAKAN ALGORITMA RANDOM FOREST DAN PENDEKATAN CONTEXT-AWARENESS Purnamasari, Etika; Hazriani, Hazriani; Arda, Abdul Latief
Information System Journal Vol. 8 No. 02 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i02.2417

Abstract

Penelitian ini bertujuan mengembangkan aplikasi Personal mobility assistant untuk mendukung mobilitas aman anak di Kota Kendari. Sistem dirancang untuk memprediksi tingkat keamanan lokasi menggunakan algoritma Random Forest berdasarkan lima fitur location context: pertemuan dengan orang asing, kepadatan lalu lintas, pencahayaan, visibilitas, dan pantauan CCTV. Dataset diperoleh dari observasi lapangan pada 96 titik lokasi dengan total 279 variasi data, kemudian diolah menggunakan Python dan diintegrasikan ke aplikasi Flutter melalui API Flask. Hasil pelatihan model menunjukkan akurasi rata-rata 94,64% dengan 5-fold cross-validation, serta akurasi 100% pada 56 data uji. Parameter pengguna dan waktu diterapkan dengan pendekatan rule based untuk memberikan respons sesuai usia dan kondisi. Uji coba aplikasi menunjukkan peringatan dalam 1 detik dan performa yang efisien. Penelitian ini membuktikan bahwa sistem mampu mengklasifikasi keamanan lokasi dan berfungsi efektif untuk meningkatkan keselamatan mobilitas anak.
Analysis of Student Behavior Based on the History of Learning Activities in the Learning Management System Using the Pearson Correlation Method Imam Akbar; Hazriani Hazriani; Abdul Latief Arda; Ita Sarmita Samad
Edumaspul: Jurnal Pendidikan Vol 8 No 1 (2024): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v8i1.7671

Abstract

The purpose of this study is to identify student learning behavior in online learning and to determine the relationship between student learning behavior and learning achievement based on learning history data (Learning logs) on the Learning Management System (LMS), including the performance of assignments and quizzes, utilization interaction features (forums and chat), as well as active access to learning resources (files and URLs). Pearson Correlation method is used to analyze the level of relationship between learning behavior and students’ achievement. The research object is 105 students at Muhammadiyah University of Enrekang who programmed introductory information technology (PTI) courses from 5 different classes but taught by the same lecturer. The total number of processed activity histories (after data preprocessing) is 6500 records, while the total number of logs before data preprocessing is 19386 records. Correlation analysis linking student behavior to student learning achievement is quite strong and unidirectional, as evidenced by the correlation value between learning behavior and student final grades which show an average number of 0.80 and all are positive, with confidence interval values reaching95%. This shows that the higher the learning activities that students participate in in online learning (the more active), the stronger the effect on student learning achievement. It also shows that student activity in completing assignments is the variable that most influences learning achievement with a correlation value of 0.88 (very strong).
Identifikasi Pola Prilaku Belajar Mahasiswa Pada Platform Learning Management System Dengan Algoritma K-Means Muhammad Alam, Ridho; Hazriani, Hazriani; Latied Arda, Abdul; Ikhwan Mardin, Muhammad
Jurnal JEETech Vol. 6 No. 1 (2025): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i1.6102

Abstract

This study aims to explain students' behavior in accessing the kalam.umi.ac.id learning platform and its impact on student academic performance. It analyzes data collected from students' access to the platform during 12 sessions in one semester for the Islamic communication subject taught by Professor Surani. The data includes students' access times, access frequency, and academic performance such as assignment grades, midterm exams, and final exams. Through data processing methods, correlation analysis, and cluster optimization, the study found a positive relationship between access times, midterm exams, final exams, and assignments with students' final grades. The higher these variables, the higher the students' final grades. However, this relationship is not always consistent across different variables. In this study, the Elbow method was used to determine the optimal number of clusters by identifying the point where the variance reduction becomes less significant. Additionally, the Sum of Square Error (SSE) was analyzed to understand the sharp change followed by a gradual decrease in the value of K until stability is reached. Clustering results using the K-Means algorithm showed the presence of three student clusters based on their learning behavior. Cluster 0 is the largest, consisting of 176 students. Cluster 1 has 57 students, and cluster 2 is the smallest with 17 students. These clusters provide insights into varying student learning patterns, including differences in final grades and access frequency. These findings can be used as a basis for developing more effective and personalized learning strategies for students.
Information Extraction from Makassar Culinary Images Using Vision Transformers and Cahya GPT-2 (Visual Question Answering Case Study Tirta Chiantalia Sharief; Hazriani; Syamsul; Anas; Yuyun
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.357

Abstract

This study examines the development of a Visual Question Answering (VQA) system to extract information from images of Makassar culinary specialties by combining the Vision Transformer (ViT) and Cahya_GPT-2 models. The main objective is to integrate visual and natural language understanding so that computers can recognize visual objects (food images) and generate relevant text descriptions. The research method uses an experimental approach with a fine-tuning process of the pre-trained ViT model as a visual encoder and Cahya_GPT-2 as a text decoder. The dataset used includes images of Makassar culinary specialties such as Coto, Konro, Pisang Epe, Barongko, and Jalangkote with question and answer (QnA) annotations. Evaluation is carried out using the ROUGE metric to assess the semantic match between the model's answers and the actual answers. The results show that the developed multimodal model is able to accurately understand the image context with an average ROUGE-L score of 0.63, indicating a good level of closeness between the model's answers and the annotations. In conclusion, the combination of ViT and Cahya_GPT-2 can be an effective approach for natural language-based visual information extraction systems, especially in the Indonesian local culinary domain
Co-Authors A Muh Fajar Maulana Natsir A. Ade Rosali Saputra Abdul Latief Arda Abdul Rokhman Abdul Rokhman Adnan Nur Agussalim, Agussalim Ahmad Firman Ahmad, Andani Anas Andani Achamad Andani Achmad Andani Achmad Andi riski ramadani APRILIANI, AULIA Aris Susanto Ayu Hasnining Baital, Muhammad Syarif Basirung Umaternate BASRI BASRI Dedi Suarna Dhimas Tribuana Elsa Sera Fajar Husain Asy'ari Hafsah HS Hafsah. HS Hairudin, Muhammad Azhar harahap , Nurlina Ariani Harahap, Amin Hasanuddin, Zulfajri Herlina Ahmad herlinah herlinah Herlinah Herlinah Imam Akbar Imam Akbar, Imam Imam T. Umagapi Ingrid Nurtanio Ita Sarmita Samad Ita Sarmita Samad, Ita Sarmita Jasman Jasman La Ija Latied Arda, Abdul Leonard Maramis Lut Faizal Mashur Razak Muh Arfah Wahlil Pratama Muhammad Alam, Ridho Muhammad Arfah Wahlil Muhammad Azhar Hairudin MUHAMMAD FUAD Muhammad Ikhwan Mardin Munthakhabah. R, Cita St. Musliadi K H Nur, Adnan Nuradha, Nuradha Nurul Azmi Omar Wahid Purnamasari, Etika riswanto riswanto Salmawati Samasil Samsart Deandi Palumery SYAMSU ALAM Syamsul Tirta Chiantalia Sharief Valensa Yosephi Wabula, Yuyun Wibowo, Deni Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun, Yuyun Zahir Zainuddin Zahir Zainuddin